CN114974538B - Ward nursing early warning management system based on big data - Google Patents
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- 230000000474 nursing effect Effects 0.000 title claims abstract description 179
- 238000005259 measurement Methods 0.000 claims abstract description 117
- 238000004458 analytical method Methods 0.000 claims abstract description 69
- 238000012544 monitoring process Methods 0.000 claims abstract description 55
- 238000013500 data storage Methods 0.000 claims abstract description 15
- 238000007726 management method Methods 0.000 claims abstract description 11
- 230000036544 posture Effects 0.000 claims description 88
- 230000036772 blood pressure Effects 0.000 claims description 40
- 230000036760 body temperature Effects 0.000 claims description 39
- 230000000284 resting effect Effects 0.000 claims description 32
- 230000036387 respiratory rate Effects 0.000 claims description 30
- 230000036391 respiratory frequency Effects 0.000 claims description 17
- 238000004364 calculation method Methods 0.000 claims description 13
- 239000000428 dust Substances 0.000 claims description 12
- 238000009530 blood pressure measurement Methods 0.000 claims description 8
- 238000009529 body temperature measurement Methods 0.000 claims description 8
- 238000009532 heart rate measurement Methods 0.000 claims description 8
- 238000000034 method Methods 0.000 claims description 7
- 230000008569 process Effects 0.000 claims description 7
- 238000012216 screening Methods 0.000 claims description 6
- 230000002159 abnormal effect Effects 0.000 abstract description 5
- 230000007547 defect Effects 0.000 abstract description 5
- 230000004044 response Effects 0.000 abstract description 4
- 238000011084 recovery Methods 0.000 description 10
- 206010011409 Cross infection Diseases 0.000 description 3
- 206010029803 Nosocomial infection Diseases 0.000 description 3
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
- A61B5/02055—Simultaneously evaluating both cardiovascular condition and temperature
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
- G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/021—Measuring pressure in heart or blood vessels
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
- A61B5/0816—Measuring devices for examining respiratory frequency
Abstract
The invention discloses a ward nursing early warning management system based on big data, which comprises a ward basic information acquisition module, a patient basic nursing monitoring analysis module, a patient physiological index nursing monitoring analysis module, a patient rest posture nursing monitoring analysis module, a patient rest environment nursing monitoring analysis module, a ward nursing early warning analysis module, an early warning display terminal and a data storage module. The physiological index measurement standard index corresponding to the patient is obtained through comprehensive analysis, so that deviation and errors caused by artificial memory are effectively avoided, certain subjectivity is eliminated, the defect that measurement is not timely and other events occur due to negligence of the work of medical staff is overcome, the abnormal response efficiency of the physiological index of the patient is greatly improved, the nursing quality and nursing effect of the patient are improved, meanwhile, the trust problem of the patient to medical staff is effectively solved, and the satisfaction degree and trust degree of the patient to hospitals and medical staff are enhanced.
Description
Technical Field
The invention relates to the technical field of ward nursing early warning, in particular to a ward nursing early warning management system based on big data.
Background
With the steady improvement of medical level and the increasing demand of more and more patients on nursing quality, the nursing quality is reduced due to objective reasons such as insufficient equipment of medical staff, and therefore, the importance of ward nursing early warning management is self-evident.
In the development of the prior art, ward nursing early warning still adopts an artificial patrol ward or adopts a mode that a patient sounds an alarm to carry out ward nursing, and obviously, the current ward nursing early warning still has the following defects:
1. at present, when basic nursing of a patient is monitored by pre-warning, the beds and clothes of the patient are regularly replaced and disinfected, and irregular replacement is not carried out according to the neatness of the beds and clothes of the patient, so that the possibility of cross infection of the patient in ward rest is increased, the effectiveness of basic nursing of the patient is reduced, the rest comfort of the patient is not high, and the basic requirement of the patient cannot be met.
2. At present, when the physiological index of a patient is monitored by pre-warning, the physiological index of the patient is often measured in a mode of manual memory of medical staff, so that great subjectivity exists, the occurrence of events such as untimely measurement and the like caused by negligence of the work of the medical staff is easy, the response efficiency of the physiological index abnormality of the patient cannot be improved, the nursing quality and the nursing effect of the patient are reduced, the trust degree of the patient to the medical staff is also influenced, and the satisfaction degree of the patient to a hospital is reduced.
3. The recuperation state of the patient directly influences the recuperation effect of the patient, and when the ward nursing is managed in advance, only basic nursing of the patient and the physiological index level of the patient are aimed at, and analysis is not carried out on the basic nursing of the patient, so that certain influence is caused on the recovery condition of the patient, the body recovery rate of the patient cannot be guaranteed, the recovery period of the patient is prolonged, and the recuperation effect of the patient is poor.
Disclosure of Invention
In order to overcome the defects in the background technology, the embodiment of the invention provides a ward nursing early warning management system based on big data, which can effectively solve the problems related to the background technology.
The aim of the invention can be achieved by the following technical scheme:
a big data based ward care early warning management system, comprising:
the ward basic information acquisition module is used for counting the number of ward, and numbering each ward as 1,2 according to a preset sequence, i..n., counting the number of patients in each ward, and numbering each patient as 1,2 according to a preset sequence, j..m;
the basic nursing monitoring analysis module is used for monitoring basic nursing of each patient in each ward through the camera, and analyzing the basic nursing of each patient in each ward to obtain basic nursing standard-reaching coefficients corresponding to each patient in each ward;
The system comprises a patient physiological index nursing monitoring analysis module, a physiological index nursing standard-reaching coefficient analysis module and a physiological index nursing standard-reaching analysis module, wherein the patient physiological index nursing monitoring analysis module is used for monitoring the measurement times of physiological indexes, the interval time of each measurement of the physiological indexes and the measured value of each measurement of the physiological indexes of each patient in each ward in a set time period, and analyzing the physiological index nursing standard-reaching coefficient of each patient in each ward, and comprises a physiological index measurement standard analysis unit, a physiological index coincidence analysis unit and a physiological index nursing standard-reaching analysis unit;
the patient rest posture nursing monitoring analysis module is used for monitoring the rest posture corresponding to each patient in each ward through the camera, and analyzing the rest posture to obtain the rest posture nursing standard-reaching coefficient corresponding to each patient in each ward;
the patient nursing environment monitoring and analyzing module is used for monitoring the nursing environment of each ward to obtain the nursing environment information of each ward, and analyzing the nursing environment information to obtain the nursing environment standard-reaching coefficient corresponding to each ward;
the ward nursing early warning analysis module is used for analyzing basic nursing standard-reaching coefficients, physiological index nursing standard-reaching coefficients, resting posture nursing standard-reaching coefficients and resting environment nursing standard-reaching coefficients corresponding to the patients in each ward to obtain an early warning set;
The early warning display terminal is used for carrying out corresponding display based on the early warning set;
the data storage module is used for storing standard measurement interval duration, standard measurement times and qualified measurement values of the physiological indexes corresponding to each patient in each ward and storing recommended rest posture images corresponding to each patient in each ward.
Preferably, the analyzing obtains the basic care standard-reaching coefficient corresponding to each patient in each ward, and the specific analyzing steps are as follows:
extracting monitoring images of basic nursing corresponding to each patient in each ward, acquiring bed images and clothes images corresponding to each patient in each ward, counting the number of spot positions on beds of each patient in each ward and the number of spot positions on clothes, acquiring spot areas of each spot position and spot areas of each spot position, and acquiring the bed use duration and clothes wearing duration corresponding to each patient in each ward from the background;
comprehensively analyzing the number of the stains on the bed corresponding to each patient in each ward, the area of the stains corresponding to each stain and the using time of the bed to obtain the standard index of the bed corresponding to each patient in each ward, and recording asi is denoted as the number of the ward, i=1, 2, & gt, n, j is denoted as the number of the patient, j=1, 2, & gt, m;
Comprehensively analyzing the number of the stains on the clothes and trousers corresponding to each patient in each ward, the areas of the stains corresponding to each stain and the wearing time of the clothes and trousers to obtain the clothes and trousers standard index corresponding to each patient in each ward, and recording as
The bed standard index and the clothes standard index corresponding to each patient in each ward are comprehensively analyzed to obtain the basic nursing standard-reaching coefficient corresponding to each patient in each ward, and the specific calculation formula is as follows Expressed as a basic care standard-reaching coefficient corresponding to the jth patient in the ith ward, beta 1 、β 2 Respectively expressed as weight factors corresponding to the set bed standard indexes and the garment standard indexes.
Preferably, the physiological indicators include heart rate, blood pressure, body temperature, and respiratory rate.
Preferably, the physiological index measurement standard analysis unit is used for calculating physiological index measurement standard indexes corresponding to each patient in each ward, and the specific calculation process is as follows:
extracting heart rate, blood pressure, body temperature and respiratory frequency from interval time of each measurement of physiological index of each patient in each wardRespectively screening out the longest measurement interval duration corresponding to heart rate, the longest measurement interval duration corresponding to blood pressure, the longest measurement interval duration corresponding to body temperature and the longest measurement interval duration corresponding to respiratory rate, and respectively recording as And->
The standard measurement interval duration of the corresponding physiological index of each patient in each ward is extracted from the data storage module, and the heart rate standard measurement interval duration, the blood pressure standard measurement interval duration, the body temperature standard measurement interval duration and the respiratory frequency standard measurement interval duration are positioned and respectively marked as X '' ij 、Y′ ij 、T′ ij And H' ij ；
According to the formulaCalculating the coincidence index of the corresponding measuring interval of each patient in each ward, < >>A compliance index expressed as a corresponding measurement interval of the jth patient in the ith ward, e expressed as a natural constant, c 1 、c 2 、c 3 、c 4 Respectively representing weight factors corresponding to preset heart rate measurement interval duration, blood pressure measurement interval duration, body temperature measurement interval duration and respiratory frequency measurement interval duration;
the heart rate measurement times, the blood pressure measurement times, the body temperature measurement times and the respiratory frequency measurement times are positioned from the measurement times of the corresponding physiological indexes of the patients in each ward in a set time period and are compared with the standard measurement times of the corresponding physiological indexes of the patients in each ward stored in a data storage module, and then the coincidence index of the corresponding measurement times of the patients in each ward is calculated and recorded as
Substituting the coincidence index of the corresponding measurement interval of each patient in each ward and the coincidence index of the measurement times into a formula Wherein, the physiological index measurement standard index corresponding to each patient in each ward is calculated>Expressed as a physiological index measurement normative index, beta, corresponding to the ith patient in the ith ward 3 、β 4 Respectively representing the weight factors corresponding to the coincidence indexes of the preset measuring intervals and the coincidence indexes of the measuring times.
Preferably, the physiological index coincidence analysis unit is configured to calculate a physiological index coincidence index corresponding to each patient in each ward, and the specific calculation steps are as follows:
screening out the maximum heart rate, the minimum heart rate, the maximum blood pressure, the minimum blood pressure, the maximum body temperature, the minimum body temperature, the maximum respiratory rate and the minimum respiratory rate from the measured values of the corresponding physiological indexes of each patient in each ward;
extracting qualified heart rate, qualified blood pressure, qualified body temperature and qualified respiratory rate corresponding to each patient in each ward from qualified measurement values of physiological indexes corresponding to each patient in each ward;
comparing the maximum heart rate, minimum heart rate, maximum blood pressure, minimum blood pressure, maximum body temperature, minimum body temperature, maximum respiratory rate and minimum respiratory rate corresponding to each patient in each ward with the corresponding qualified heart rate, qualified blood pressure, qualified body temperature and qualified respiratory rate to obtain the corresponding physiological index coincidence index of each patient in each ward, and recording as
Preferably, the physiological index nursing standard-reaching analysis unit is used for calculating physiological index nursing standard-reaching coefficients corresponding to each patient in each wardThe specific calculation formula is that Expressed as the physiological index care standard index corresponding to the jth patient in the ith ward, tau 1 、τ 2 Respectively representing the set physiological index measurement standard index and the weight factor corresponding to the physiological index coincidence index.
Preferably, the analyzing obtains the corresponding nursing posture care standard-reaching coefficient of each patient in each ward, and the specific analyzing steps are as follows:
extracting monitoring images of the corresponding rest postures of all patients in all ward, marking the monitoring images as actual rest posture images, performing overlapping comparison on the actual rest posture images corresponding to all patients in all ward and the recommended rest posture images corresponding to all patients in all ward stored in a data storage module to obtain overlapping areas, and marking the overlapping areas as
Extracting the position of the center point of the outline of the actual resting posture and the position of the center point of the outline of the recommended resting posture corresponding to each patient in each ward from the image of the actual resting posture and the image of the recommended resting posture corresponding to each patient in each ward, further obtaining the distance between the position of the center point of the outline of the actual resting posture and the position of the center point of the outline of the recommended resting posture corresponding to each patient in each ward, taking the distance as the moving distance, and recording as the moving distance
According to the formulaCalculating the nursing standard reaching coefficient of the rest posture corresponding to each patient in each ward, and (2)>The nursing standard reaching coefficient is expressed as a nursing posture corresponding to the jth patient in the ith ward, M' ij 、L′ ij Respectively expressed as the outline area of the recommended rest posture, the allowed movement distance and χ of the jth patient in the ith ward 1 、χ 2 The respective coefficients are expressed as coefficient factors corresponding to the set overlapping area and the set moving distance.
Preferably, the analysis obtains the standard-reaching coefficient of the rest environment care corresponding to each ward, and the specific analysis steps are as follows:
extracting dust concentration, noise value, temperature and humidity from the rest environment information in each ward and respectively marking as f i 、g i 、k i And p i ；
According to the formulaCalculating to obtain the corresponding nursing environment nursing standard-reaching coefficient of each ward, psi i The standard-reaching coefficient of the nursing environment corresponding to the ith ward is expressed as f ', g', k ', p' which are respectively expressed as the set allowable dust concentration, allowable noise value, allowable temperature and allowable humidity, v 1 、v 2 、v 3 、v 4 Respectively expressed as coefficient factors corresponding to the set dust concentration, noise value, temperature and humidity.
Preferably, the early warning set comprises an early warning ward set and an early warning patient set, and the specific acquisition process is as follows:
Comparing the standard-reaching basic care coefficient corresponding to each patient in each ward with a set standard-reaching basic care coefficient threshold, and if the standard-reaching basic care coefficient corresponding to a patient in a certain ward is smaller than the standard-reaching basic care coefficient threshold, marking the ward as an early-warning ward and marking the patient as an early-warning patient;
comparing the physiological index nursing standard reaching coefficient corresponding to each patient in each ward with a set physiological index nursing standard reaching coefficient threshold, and if the physiological index nursing standard reaching coefficient corresponding to a certain patient in a certain ward is smaller than the physiological index nursing standard reaching coefficient threshold, marking the ward as an early warning ward, and marking the patient as an early warning patient;
comparing the standard-reaching coefficient of the rest posture care corresponding to each patient in each ward with a set standard-reaching coefficient threshold of the rest posture care, and if the standard-reaching coefficient of the rest posture care corresponding to a patient in a certain ward is smaller than the standard-reaching coefficient threshold of the rest posture care, marking the ward as an early-warning ward, and marking the patient as an early-warning patient;
comparing the standard-reaching coefficient of the rest environment care corresponding to each ward with a set standard-reaching coefficient threshold of the rest environment care, and if the standard-reaching coefficient of the rest environment care corresponding to a certain ward is smaller than the standard-reaching coefficient threshold of the rest environment care, marking the ward as an early-warning ward;
The number of the early warning wards and the number of the early warning patients are counted, an early warning ward set and an early warning patient set are respectively constructed, and the early warning ward set and the early warning patient set are combined to generate an early warning set.
Compared with the prior art, the embodiment of the invention has at least the following advantages or beneficial effects:
(1) According to the invention, the image acquisition is carried out on the bed and the clothes of the patient through the camera, the bed standard index and the clothes standard index are obtained through analysis, the basic nursing standard coefficient corresponding to the patient is comprehensively obtained, and further, the patient with unqualified basic nursing standard coefficient is replaced and disinfected in time, so that the possibility of cross infection of the patient is avoided, the quality of basic nursing of the ward is improved, the basic requirement of the patient is met to the greatest extent, and the problem of low comfort of patient rest is effectively solved.
(2) According to the invention, the physiological index measurement standard index corresponding to the patient is obtained by comprehensively analyzing the coincidence index of the corresponding measurement interval and the coincidence index of the measurement times of the patient, so that deviation and errors caused by artificial memory are effectively avoided, a certain subjectivity is eliminated, the defect that measurement is not timely and other events occur due to negligence of the work of medical staff is overcome, the response efficiency of abnormal physiological indexes of the patient is greatly improved, the nursing quality and nursing effect of the patient are improved, the trust problem of the patient to the medical staff is effectively solved, and the satisfaction degree and the trust degree of the patient to hospitals and the medical staff are enhanced.
(3) According to the invention, the camera is used for collecting the images of the rest postures of the patient, and the rest posture nursing standard index corresponding to the patient is obtained through analysis, so that whether the rest postures of the patient are consistent with the standard rest postures corresponding to the illness states of the patient can be intuitively known, the correctness of the rest postures of the patient is effectively ensured, the illness state recovery of the patient is facilitated, the recovery period of the patient is shortened, the body recovery rate of the patient is effectively ensured, the effectiveness and the reliability of the rest posture nursing of the patient are greatly improved, and the sick house nursing early warning is more convincing.
(4) According to the invention, the nursing environment in the ward is monitored and analyzed to obtain the nursing environment standard reaching index corresponding to the ward, so that a good environment is provided for the patient's nursing, the negative influence of the change of the nursing environment on the patient's condition is avoided, a reliable reference basis is provided for medical staff to reasonably adjust the ward environment, the patient's rehabilitation needs are met, the adjusting rate of the abnormal ward environment is ensured, the comfort of the ward environment is maintained, and the patient's demand on the ward environment is met.
Drawings
The invention will be further described with reference to the accompanying drawings, in which embodiments do not constitute any limitation of the invention, and other drawings can be obtained by one of ordinary skill in the art without inventive effort from the following drawings.
FIG. 1 is a schematic diagram of the system module connection of the present invention.
Fig. 2 is a schematic connection diagram of the patient physiological index nursing monitoring and analyzing module according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention provides a ward nursing early warning management system based on big data, which comprises a ward basic information acquisition module, a patient basic nursing monitoring analysis module, a patient physiological index nursing monitoring analysis module, a patient resting posture nursing monitoring analysis module, a patient resting environment nursing monitoring analysis module, a ward nursing early warning analysis module, an early warning display terminal and a data storage module.
The ward basic information acquisition module is respectively connected with the patient basic nursing monitoring analysis module, the patient physiological index nursing monitoring analysis module, the patient resting posture nursing monitoring analysis module and the patient resting environment nursing monitoring analysis module, the ward nursing early warning analysis module is respectively connected with the patient basic nursing monitoring analysis module, the patient physiological index nursing monitoring analysis module, the patient resting posture nursing monitoring analysis module, the patient resting environment nursing monitoring analysis module and the early warning display terminal, and the data storage module is respectively connected with the patient physiological index nursing monitoring analysis module and the patient resting posture nursing monitoring analysis module.
The ward basic information acquisition module is used for counting the number of ward, and numbering each ward as 1,2 according to a preset sequence, i..n., counting the number of patients in each ward, and numbering each patient as 1,2 according to a preset sequence.
And the patient basic nursing monitoring analysis module is used for monitoring basic nursing of each patient in each ward and analyzing the basic nursing to obtain basic nursing standard reaching coefficients corresponding to each patient in each ward.
Preferably, the analyzing obtains the basic care standard-reaching coefficient corresponding to each patient in each ward, and the specific analyzing steps are as follows:
extracting monitoring images of basic nursing corresponding to each patient in each ward, acquiring bed images and clothes images corresponding to each patient in each ward, counting the number of spot positions on beds of each patient in each ward and the number of spot positions on clothes, acquiring spot areas of each spot position and spot areas of each spot position, and acquiring the bed use duration and clothes wearing duration corresponding to each patient in each ward from the background;
comprehensively analyzing the number of the stains on the bed corresponding to each patient in each ward, the area of the stains corresponding to each stain and the using time of the bed to obtain the standard index of the bed corresponding to each patient in each ward, and recording as i is denoted as the number of the ward, i=1, 2, & gt, n, j is denoted as the number of the patient, j=1, 2, & gt, m;
it should be noted that, according to the formulaCalculating to obtain the standard index z of the bed corresponding to each patient in each ward ij Expressed as the number of stains on the bed corresponding to the jth patient in the ith ward, z 0 Expressed as the number of allowed stains set, +.>The stain area indicated as the jth patient in the ith ward corresponding to the jth stain, w indicated as the number of the stain, w=1, 2>Denoted as the usage time of the corresponding bed of the jth patient in the ith ward, y' denoted as the preset reference bed usage time, alpha 1 、α 2 、α 3 The number of the stains, the area of the stains and the length of the bed are respectively expressed as the corresponding influence factors.
Comprehensively analyzing the number of the stains on the clothes and trousers corresponding to each patient in each ward, the areas of the stains corresponding to each stain and the wearing time of the clothes and trousers to obtain the clothes and trousers standard index corresponding to each patient in each ward, and recording as
It should be noted that, according to the formulaCalculating to obtain the clothes and trousers standard index corresponding to each patient in each ward, d ij Expressed as the number of spots on the garment corresponding to the jth patient in the ith ward,/for the garment >The spot area expressed as the spot area at the jth patient corresponding to the b spot in the ith ward, b expressed as the number at the spot, b=1, 2>Expressed as the wearing time length of the corresponding clothes and trousers of the jth patient in the ith ward, d 0 Expressed as the number of preset allowed spots, S 'expressed as the preset allowed spot area, u' expressed as the preset reference wear duration of the garment, alpha 4 、α 5 、α 6 Respectively expressed as the set quantity of the spots, the spot area and the corresponding influence factors of the wearing time of the clothes and trousers.
The bed standard index and the clothes standard index corresponding to each patient in each ward are comprehensively analyzed to obtain the basic nursing standard-reaching coefficient corresponding to each patient in each ward, and the specific calculation formula is as follows Expressed as a basic care standard-reaching coefficient corresponding to the jth patient in the ith ward, beta 1 、β 2 Respectively expressed as weight factors corresponding to the set bed standard indexes and the garment standard indexes.
The invention acquires images of the bed and the clothes of the patient through the camera, obtains the standard index of the bed and the standard index of the clothes through analysis, comprehensively obtains the standard-reaching coefficient of basic nursing corresponding to the patient, further timely changes and disinfects the patient with unqualified standard-reaching coefficient of the basic nursing, avoids the possibility of cross infection of the patient, improves the quality of basic nursing of the ward, meets the basic requirement of the patient to the greatest extent, and effectively solves the problem of low comfort of patient rest.
Referring to fig. 2, a patient physiological index nursing monitoring and analyzing module is configured to monitor, in a set period of time, a measurement number of times of physiological indexes corresponding to each patient in each ward, an interval duration of each measurement of the physiological indexes, and a measurement value of each measurement of the physiological indexes, and thereby analyze to obtain a physiological index nursing standard reaching coefficient corresponding to each patient in each ward, where the patient physiological index nursing monitoring and analyzing module includes a physiological index measurement standard analyzing unit, a physiological index coincidence analyzing unit, and a physiological index nursing standard analyzing unit.
Preferably, the physiological indicators include heart rate, blood pressure, body temperature, and respiratory rate.
Preferably, the physiological index measurement standard analysis unit is used for calculating physiological index measurement standard indexes corresponding to each patient in each ward, and the specific calculation process is as follows:
extracting interval durations of heart rate, blood pressure, body temperature and respiratory frequency corresponding to each measurement from interval durations of each measurement of physiological indexes of each patient in each ward, respectively screening out the longest measurement interval duration corresponding to the heart rate, the longest measurement interval duration corresponding to the blood pressure, the longest measurement interval duration corresponding to the body temperature and the longest measurement interval duration corresponding to the respiratory frequency from the interval durations, and respectively recording as And->
Extracting standard measurement interval duration of corresponding physiological indexes of each patient in each ward from a data storage module, and locating heart rate standard measurement interval duration and blood pressure standard measurement interval durationThe body temperature standard measurement interval duration and the respiratory frequency standard measurement interval duration are respectively marked as X' ij 、Y′ ij 、T′ ij And H' ij ；
According to the formulaCalculating the coincidence index of the corresponding measuring interval of each patient in each ward, < >>A compliance index expressed as a corresponding measurement interval of the jth patient in the ith ward, e expressed as a natural constant, c 1 、c 2 、c 3 、c 4 Respectively representing weight factors corresponding to preset heart rate measurement interval duration, blood pressure measurement interval duration, body temperature measurement interval duration and respiratory frequency measurement interval duration;
the heart rate measurement times, the blood pressure measurement times, the body temperature measurement times and the respiratory frequency measurement times are positioned from the measurement times of the corresponding physiological indexes of the patients in each ward in a set time period and are compared with the standard measurement times of the corresponding physiological indexes of the patients in each ward stored in a data storage module, and then the coincidence index of the corresponding measurement times of the patients in each ward is calculated and recorded as
It should be noted that, according to the formula Calculating the coincidence index of the corresponding measurement times of each patient in each ward>Respectively expressed as heart rate measurement times, blood pressure measurement times, body temperature measurement times and respiratory frequency measurement times corresponding to the jth patient in the ith ward, and X ij 、Y″ ij 、T″ ij 、H″ ij Separate tableThe standard measurement times of heart rate, standard measurement times of blood pressure, standard measurement times of body temperature and standard measurement times of respiratory rate corresponding to the jth patient in the ith ward are shown as c 5 、c 6 、c 7 、c 8 Respectively expressed as the set weight factors corresponding to the heart rate measurement times, the blood pressure measurement times, the body temperature measurement times and the respiratory frequency measurement times.
Substituting the coincidence index of the corresponding measurement interval of each patient in each ward and the coincidence index of the measurement times into a formulaWherein, the physiological index measurement standard index corresponding to each patient in each ward is calculated>Expressed as a physiological index measurement normative index, beta, corresponding to the ith patient in the ith ward 3 、β 4 Respectively representing the weight factors corresponding to the coincidence indexes of the preset measuring intervals and the coincidence indexes of the measuring times.
It should be noted that, the invention obtains the physiological index measurement standard index corresponding to the patient by comprehensively analyzing the coincidence index of the corresponding measurement interval of the patient and the coincidence index of the measurement times, effectively avoids the deviation and error caused by the artificial memory, eliminates certain subjectivity, makes up the defect that the measurement is not timely and other events occur due to the negligence of the work of medical staff, greatly improves the response efficiency of the abnormal physiological index of the patient, improves the nursing quality and nursing effect of the patient, and simultaneously effectively solves the trust problem of the patient to the medical staff, and enhances the satisfaction degree and the trust degree of the patient to hospitals and the medical staff.
Preferably, the physiological index coincidence analysis unit is configured to calculate a physiological index coincidence index corresponding to each patient in each ward, and the specific calculation steps are as follows:
screening out the maximum heart rate, the minimum heart rate, the maximum blood pressure, the minimum blood pressure, the maximum body temperature, the minimum body temperature, the maximum respiratory rate and the minimum respiratory rate from the measured values of the corresponding physiological indexes of each patient in each ward;
extracting qualified heart rate, qualified blood pressure, qualified body temperature and qualified respiratory rate corresponding to each patient in each ward from qualified measurement values of physiological indexes corresponding to each patient in each ward;
comparing the maximum heart rate, minimum heart rate, maximum blood pressure, minimum blood pressure, maximum body temperature, minimum body temperature, maximum respiratory rate and minimum respiratory rate corresponding to each patient in each ward with the corresponding qualified heart rate, qualified blood pressure, qualified body temperature and qualified respiratory rate to obtain the corresponding physiological index coincidence index of each patient in each ward, and recording as
It should be noted that, the specific calculation formula of the corresponding physiological index coincidence index of each patient in each ward is as follows Respectively expressed as heart rate uniformity coefficient, blood pressure uniformity coefficient, body temperature uniformity coefficient and respiratory rate uniformity coefficient corresponding to the jth patient in the ith ward, r 1 、r 2 、r 3 、r 4 Respectively expressed as the set heart rate uniformity coefficient, blood pressure uniformity coefficient, body temperature uniformity coefficient and respiratory rate uniformity coefficient.
Wherein, respectively expressed as maximum heart rate and minimum heart rate corresponding to the jth patient in the ith ward, E' ij Expressed as the qualified heart rate corresponding to the jth patient in the ith ward, < + >>The allowable measurement heart rate difference, q, corresponding to the jth patient in the ith ward is shown as the set 1 、q 2 Respectively expressed as the set influence factors corresponding to the maximum heart rate and the minimum heart rate.
Respectively expressed as maximum blood pressure and minimum blood pressure corresponding to the jth patient in the ith ward, F' ij Indicated as the qualified blood pressure corresponding to the jth patient in the ith ward,indicated as the allowable measurement blood pressure difference corresponding to the jth patient in the set ith ward, q 3 、q 4 The blood pressure is expressed as the influence factors corresponding to the set maximum blood pressure and the set minimum blood pressure.
Respectively expressed as maximum body temperature and minimum body temperature corresponding to the jth patient in the ith ward, G' ij Indicated as the qualified body temperature corresponding to the jth patient in the ith ward,the allowable measured body temperature difference, q, corresponding to the jth patient in the ith ward is shown as 5 、q 6 Respectively expressed as the set influence factors corresponding to the maximum body temperature and the minimum body temperature.
Respectively expressed as the maximum respiratory rate and the minimum respiratory rate corresponding to the jth patient in the ith ward, I' ij Expressed as the qualified respiratory rate corresponding to the jth patient in the ith ward,/patient>Indicated as the allowable measurement respiratory frequency difference, q, corresponding to the jth patient in the ith ward 7 、q 8 Respectively expressed as the set influence factors corresponding to the maximum respiratory rate and the minimum respiratory rate.
Preferably, the physiological index nursing standard-reaching analysis unit is used for calculating physiological index nursing standard-reaching coefficients corresponding to each patient in each ward, and the specific calculation formula is as follows Expressed as the physiological index care standard index corresponding to the jth patient in the ith ward, tau 1 、τ 2 Respectively representing the set physiological index measurement standard index and the weight factor corresponding to the physiological index coincidence index.
The patient rest posture nursing monitoring analysis module is used for monitoring the rest posture corresponding to each patient in each ward through the camera and analyzing the rest posture corresponding to each patient in each ward to obtain the rest posture nursing standard reaching coefficient.
Preferably, the analyzing obtains the corresponding nursing posture care standard-reaching coefficient of each patient in each ward, and the specific analyzing steps are as follows:
extracting monitoring images of the corresponding rest postures of all patients in all ward, marking the monitoring images as actual rest posture images, performing overlapping comparison on the actual rest posture images corresponding to all patients in all ward and the recommended rest posture images corresponding to all patients in all ward stored in a data storage module to obtain overlapping areas, and marking the overlapping areas as
Extracting the position of the center point of the outline of the actual resting posture and the position of the center point of the outline of the recommended resting posture corresponding to each patient in each ward from the image of the actual resting posture and the image of the recommended resting posture corresponding to each patient in each ward, further obtaining the distance between the position of the center point of the outline of the actual resting posture and the position of the center point of the outline of the recommended resting posture corresponding to each patient in each ward, taking the distance as the moving distance, and recording as the moving distance
According to the formulaCalculating the nursing standard reaching coefficient of the rest posture corresponding to each patient in each ward, and (2)>The nursing standard reaching coefficient is expressed as a nursing posture corresponding to the jth patient in the ith ward, M' ij 、L′ ij Respectively expressed as the outline area of the recommended rest posture, the allowed movement distance and χ of the jth patient in the ith ward 1 、χ 2 The respective coefficients are expressed as coefficient factors corresponding to the set overlapping area and the set moving distance.
The invention can acquire images of the rest postures of the patient through the camera, and obtain the rest posture nursing standard index corresponding to the patient through analysis, so that whether the rest postures of the patient are consistent with the standard rest postures corresponding to the illness states of the patient can be intuitively known, the correctness of the rest postures of the patient is effectively ensured, the illness state recovery of the patient is facilitated, the recovery period of the patient is shortened, the body recovery rate of the patient is effectively ensured, the effectiveness and the reliability of the rest posture nursing of the patient are greatly improved, and the sick house nursing early warning is more convincing.
The patient nursing environment monitoring and analyzing module is used for monitoring the nursing environment of each ward to obtain the nursing environment information of each ward, and analyzing the nursing environment information to obtain the nursing environment standard-reaching coefficient corresponding to each ward.
It should be noted that the specific monitoring process of the resting environment is as follows:
detecting the dust concentration in each ward by a dust concentration tester to obtain the dust concentration corresponding to each ward;
detecting the noise of each ward by using a noise sensor to obtain the corresponding noise of each ward;
the temperature and the humidity of each ward are detected by the temperature sensor and the humidity sensor respectively, and the temperature and the humidity corresponding to each ward are obtained.
Preferably, the analysis obtains the standard-reaching coefficient of the rest environment care corresponding to each ward, and the specific analysis steps are as follows:
extracting dust concentration, noise value, temperature and humidity from the rest environment information in each ward and respectively marking as f i 、g i 、k i And p i ；
According to the formulaCalculating to obtain the corresponding nursing environment nursing standard-reaching coefficient of each ward, psi i The standard-reaching coefficient of the nursing environment corresponding to the ith ward is expressed as f ', g', k ', p' which are respectively expressed as the set allowable dust concentration, allowable noise value, allowable temperature and allowable humidity, v 1 、v 2 、v 3 、v 4 Respectively expressed as coefficient factors corresponding to the set dust concentration, noise value, temperature and humidity.
It is to be noted that the invention monitors the nursing environment in the ward and analyzes to obtain the nursing environment standard reaching index corresponding to the ward, thereby providing a good environment for the patient's nursing, avoiding the negative influence of the change of the nursing environment on the patient's condition, providing a reliable reference for medical staff to reasonably adjust the ward environment, meeting the recovery requirement of the patient, ensuring the adjusting rate of the abnormal ward environment, maintaining the comfort of the ward environment, and meeting the requirement of the patient on the ward environment.
And the ward nursing early warning analysis module is used for analyzing basic nursing standard-reaching coefficients, physiological index nursing standard-reaching coefficients, resting posture nursing standard-reaching coefficients and resting environment nursing standard-reaching coefficients corresponding to the ward to obtain an early warning set.
Preferably, the early warning set comprises an early warning ward set and an early warning patient set, and the specific acquisition process is as follows:
comparing the standard-reaching basic care coefficient corresponding to each patient in each ward with a set standard-reaching basic care coefficient threshold, and if the standard-reaching basic care coefficient corresponding to a patient in a certain ward is smaller than the standard-reaching basic care coefficient threshold, marking the ward as an early-warning ward and marking the patient as an early-warning patient;
Comparing the physiological index nursing standard reaching coefficient corresponding to each patient in each ward with a set physiological index nursing standard reaching coefficient threshold, and if the physiological index nursing standard reaching coefficient corresponding to a certain patient in a certain ward is smaller than the physiological index nursing standard reaching coefficient threshold, marking the ward as an early warning ward, and marking the patient as an early warning patient;
comparing the standard-reaching coefficient of the rest posture care corresponding to each patient in each ward with a set standard-reaching coefficient threshold of the rest posture care, and if the standard-reaching coefficient of the rest posture care corresponding to a patient in a certain ward is smaller than the standard-reaching coefficient threshold of the rest posture care, marking the ward as an early-warning ward, and marking the patient as an early-warning patient;
comparing the standard-reaching coefficient of the rest environment care corresponding to each ward with a set standard-reaching coefficient threshold of the rest environment care, and if the standard-reaching coefficient of the rest environment care corresponding to a certain ward is smaller than the standard-reaching coefficient threshold of the rest environment care, marking the ward as an early-warning ward;
the number of the early warning wards and the number of the early warning patients are counted, an early warning ward set and an early warning patient set are respectively constructed, and the early warning ward set and the early warning patient set are combined to generate an early warning set.
And the early warning display terminal is used for carrying out corresponding display based on the early warning set.
The data storage module is used for storing standard measurement interval duration, standard measurement times and qualified measurement values of the physiological indexes corresponding to each patient in each ward and storing recommended rest posture images corresponding to each patient in each ward.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
Claims (4)
1. Ward nursing early warning management system based on big data, characterized by comprising:
the ward basic information acquisition module is used for counting the number of ward, and numbering each ward as 1,2 according to a preset sequence, i..n., counting the number of patients in each ward, and numbering each patient as 1,2 according to a preset sequence, j..m;
the basic nursing monitoring analysis module is used for monitoring basic nursing of each patient in each ward through the camera, and analyzing the basic nursing of each patient in each ward to obtain basic nursing standard-reaching coefficients corresponding to each patient in each ward;
The system comprises a patient physiological index nursing monitoring analysis module, a physiological index nursing standard-reaching coefficient analysis module and a physiological index nursing standard-reaching analysis module, wherein the patient physiological index nursing monitoring analysis module is used for monitoring the measurement times of physiological indexes, the interval time of each measurement of the physiological indexes and the measured value of each measurement of the physiological indexes of each patient in each ward in a set time period, and analyzing the physiological index nursing standard-reaching coefficient of each patient in each ward, and comprises a physiological index measurement standard analysis unit, a physiological index coincidence analysis unit and a physiological index nursing standard-reaching analysis unit;
the patient rest posture nursing monitoring analysis module is used for monitoring the rest posture corresponding to each patient in each ward through the camera, and analyzing the rest posture to obtain the rest posture nursing standard-reaching coefficient corresponding to each patient in each ward;
the patient nursing environment monitoring and analyzing module is used for monitoring the nursing environment of each ward to obtain the nursing environment information of each ward, and analyzing the nursing environment information to obtain the nursing environment standard-reaching coefficient corresponding to each ward;
the ward nursing early warning analysis module is used for analyzing basic nursing standard-reaching coefficients, physiological index nursing standard-reaching coefficients, resting posture nursing standard-reaching coefficients and resting environment nursing standard-reaching coefficients corresponding to the patients in each ward to obtain an early warning set;
The early warning display terminal is used for carrying out corresponding display based on the early warning set;
the data storage module is used for storing standard measurement interval duration, standard measurement times and qualified measurement values of physiological indexes corresponding to each patient in each ward and storing recommended rest posture images corresponding to each patient in each ward;
the basic nursing standard-reaching coefficient corresponding to each patient in each ward is obtained through analysis, and the specific analysis steps are as follows:
extracting monitoring images of basic nursing corresponding to each patient in each ward, acquiring bed images and clothes images corresponding to each patient in each ward, counting the number of spot positions on beds of each patient in each ward and the number of spot positions on clothes, acquiring spot areas of each spot position and spot areas of each spot position, and acquiring the bed use duration and clothes wearing duration corresponding to each patient in each ward from the background;
comprehensively analyzing the number of the stains on the bed corresponding to each patient in each ward, the area of the stains corresponding to each stain and the using time of the bed to obtain the standard index of the bed corresponding to each patient in each ward, and recording asI is denoted as the number of the ward, i=1, 2, & gt, n, j is denoted as the number of the patient, j=1, 2, & gt, m;
Corresponding clothes and trousers for each patient in each wardComprehensively analyzing the number of the spots, the corresponding spot areas of the spots and the wearing time of the clothes and trousers to obtain the clothes and trousers standard index corresponding to each patient in each ward, and recording as；
The bed standard index and the clothes standard index corresponding to each patient in each ward are comprehensively analyzed to obtain the basic nursing standard-reaching coefficient corresponding to each patient in each ward, and the specific calculation formula is as follows，/>Expressed as a basic care standard-of-care coefficient corresponding to the jth patient in the ith ward,/for>Respectively representing the weight factors corresponding to the set bed standard indexes and the garment standard indexes;
the early warning set comprises an early warning ward set and an early warning patient set, and the specific acquisition process comprises the following steps:
comparing the standard-reaching basic care coefficient corresponding to each patient in each ward with a set standard-reaching basic care coefficient threshold, and if the standard-reaching basic care coefficient corresponding to a patient in a certain ward is smaller than the standard-reaching basic care coefficient threshold, marking the ward as an early-warning ward and marking the patient as an early-warning patient;
the physiological index measurement standard analysis unit is used for calculating physiological index measurement standard indexes corresponding to each patient in each ward, and the specific calculation process is as follows:
Extracting interval durations of heart rate, blood pressure, body temperature and respiratory frequency corresponding to each measurement from interval durations of each measurement of physiological indexes corresponding to each patient in each ward, and respectively screening out the longest measurement interval duration corresponding to the heart rate, the longest measurement interval duration corresponding to the blood pressure, the longest measurement interval duration corresponding to the body temperature and the respiratory frequency pair from the interval durationsThe corresponding longest measurement interval duration is respectively recorded as；
The standard measurement interval duration of the corresponding physiological index of each patient in each ward is extracted from the data storage module, and the heart rate standard measurement interval duration, the blood pressure standard measurement interval duration, the body temperature standard measurement interval duration and the respiratory frequency standard measurement interval duration are positioned and respectively recorded as；
According to the formulaCalculating the coincidence index of the corresponding measuring interval of each patient in each ward, < >>The compliance index, e, expressed as the corresponding measurement interval of the jth patient in the ith ward, e, expressed as a natural constant,/->Respectively representing weight factors corresponding to preset heart rate measurement interval duration, blood pressure measurement interval duration, body temperature measurement interval duration and respiratory frequency measurement interval duration;
the heart rate measurement times, the blood pressure measurement times, the body temperature measurement times and the respiratory frequency measurement times are positioned from the measurement times of the corresponding physiological indexes of the patients in each ward in a set time period and are compared with the standard measurement times of the corresponding physiological indexes of the patients in each ward stored in a data storage module, and then the coincidence index of the corresponding measurement times of the patients in each ward is calculated and recorded as ；
Generating the coincidence index of the corresponding measurement interval of each patient in each ward and the coincidence index of the measurement timesEnter into the formulaWherein, the physiological index measurement standard index corresponding to each patient in each ward is calculated>Expressed as a physiological index measurement specification index corresponding to the ith patient in the ith ward,/patient room>Respectively representing the weight factors corresponding to the coincidence indexes of the preset measuring interval and the coincidence indexes of the measuring times;
the physiological index coincidence analysis unit is used for calculating physiological index coincidence indexes corresponding to each patient in each ward, and the specific calculation steps are as follows:
screening out the maximum heart rate, the minimum heart rate, the maximum blood pressure, the minimum blood pressure, the maximum body temperature, the minimum body temperature, the maximum respiratory rate and the minimum respiratory rate from the measured values of the corresponding physiological indexes of each patient in each ward;
extracting qualified heart rate, qualified blood pressure, qualified body temperature and qualified respiratory rate corresponding to each patient in each ward from qualified measurement values of physiological indexes corresponding to each patient in each ward;
comparing the maximum heart rate, minimum heart rate, maximum blood pressure, minimum blood pressure, maximum body temperature, minimum body temperature, maximum respiratory rate and minimum respiratory rate corresponding to each patient in each ward with the corresponding qualified heart rate, qualified blood pressure, qualified body temperature and qualified respiratory rate to obtain the corresponding physiological index coincidence index of each patient in each ward, and recording as ；
The physiological index nursing standard-reaching analysis unit is used for calculating physiological index nursing standard-reaching coefficients corresponding to each patient in each ward, and the specific calculation formula is as follows，/>Expressed as the physiological index care standard index corresponding to the jth patient in the ith ward,/for the patient in the ith ward>Respectively representing the set physiological index measurement standard index and the weight factor corresponding to the physiological index coincidence index;
the early warning set comprises an early warning ward set and an early warning patient set, and the specific acquisition process of the early warning ward set comprises the following steps:
comparing the physiological index nursing standard reaching coefficient corresponding to each patient in each ward with a set physiological index nursing standard reaching coefficient threshold, and if the physiological index nursing standard reaching coefficient corresponding to a certain patient in a certain ward is smaller than the physiological index nursing standard reaching coefficient threshold, marking the ward as an early warning ward, and marking the patient as an early warning patient;
comparing the standard-reaching coefficient of the rest posture care corresponding to each patient in each ward with a set standard-reaching coefficient threshold of the rest posture care, and if the standard-reaching coefficient of the rest posture care corresponding to a patient in a certain ward is smaller than the standard-reaching coefficient threshold of the rest posture care, marking the ward as an early-warning ward, and marking the patient as an early-warning patient;
Comparing the standard-reaching coefficient of the rest environment care corresponding to each ward with a set standard-reaching coefficient threshold of the rest environment care, and if the standard-reaching coefficient of the rest environment care corresponding to a certain ward is smaller than the standard-reaching coefficient threshold of the rest environment care, marking the ward as an early-warning ward;
the number of the early warning wards and the number of the early warning patients are counted, an early warning ward set and an early warning patient set are respectively constructed, and the early warning ward set and the early warning patient set are combined to generate an early warning set.
2. The big data based ward care early warning management system of claim 1, wherein: the physiological indicators include heart rate, blood pressure, body temperature, and respiratory rate.
3. The big data based ward care early warning management system of claim 1, wherein: the analysis obtains the corresponding nursing standard-reaching coefficient of the rest posture of each patient in each ward, and the specific analysis steps are as follows:
extracting monitoring images of the corresponding rest postures of all patients in all ward, marking the monitoring images as actual rest posture images, performing overlapping comparison on the actual rest posture images corresponding to all patients in all ward and the recommended rest posture images corresponding to all patients in all ward stored in a data storage module to obtain overlapping areas, and marking the overlapping areas as ；
Extracting the position of the center point of the outline of the actual resting posture and the position of the center point of the outline of the recommended resting posture corresponding to each patient in each ward from the image of the actual resting posture and the image of the recommended resting posture corresponding to each patient in each ward, further obtaining the distance between the position of the center point of the outline of the actual resting posture and the position of the center point of the outline of the recommended resting posture corresponding to each patient in each ward, taking the distance as the moving distance, and recording as the moving distance；
According to the formulaCalculating the nursing standard reaching coefficient of the rest posture corresponding to each patient in each ward, and (2)>The nursing standard-reaching coefficient of the posture of rest corresponding to the jth patient in the ith ward is expressed as +.>Respectively expressed as the outline area of the recommended rest posture corresponding to the jth patient in the ith ward,Allowed distance of movement +.>The respective coefficients are expressed as coefficient factors corresponding to the set overlapping area and the set moving distance.
4. The big data based ward care early warning management system of claim 1, wherein: the analysis obtains the standard-reaching coefficient of the nursing environment corresponding to each ward, and the specific analysis steps are as follows:
extracting dust concentration, noise value, temperature and humidity from the rest environment information in each ward, and respectively recording as ；
According to the formulaCalculating to obtain the corresponding nursing environment nursing standard-reaching coefficient of each ward>Expressed as the standard-reaching coefficient of the nursing environment corresponding to the ith ward,respectively expressed as a set allowable dust concentration, allowable noise value, allowable temperature, allowable humidity,respectively expressed as coefficient factors corresponding to the set dust concentration, noise value, temperature and humidity.
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